class_docstr: The neural collaborative filtering model used for recommendation.
  
  # Arguments
  user_count: The number of users. Positive int.
  item_count: The number of classes. Positive int.
  class_num: The number of classes. Positive int.
  user_embed: Units of user embedding. Positive int. Default is 20.
  item_embed: itemEmbed Units of item embedding. Positive int. Default is 20.
  hidden_layers: Units of hidden layers for MLP. Tuple of positive int. Default is (40, 20, 10).
  include_mf: Whether to include Matrix Factorization. Boolean. Default is True.
  mf_embed: Units of matrix factorization embedding. Positive int. Default is 20.
code: def __init__(
      self,
      user_count,
      item_count,
      class_num,
      user_embed=20,
      item_embed=20,
      hidden_layers=[40, 20, 10],
      include_mf=True,
      mf_embed=20,
      bigdl_type="float",
  ):
      self.user_count = int(user_count)
      self.item_count = int(item_count)
      self.class_num = int(class_num)
      self.user_embed = int(user_embed)
      self.item_embed = int(item_embed)
      self.hidden_layers = [int(unit) for unit in hidden_layers]
      self.include_mf = include_mf
      self.mf_embed = int(mf_embed)
      self.bigdl_type = bigdl_type
      self.model = self.build_model()
      super(NeuralCF, self).__init__(
          None,
          self.bigdl_type,
          self.user_count,
          self.item_count,
          self.class_num,
          self.user_embed,
          self.item_embed,
          self.hidden_layers,
          self.include_mf,
          self.mf_embed,
          self.model,
      )
  
file_path: analytics-zoo/pyzoo/zoo/models/recommendation/neuralcf.py
imports: import sys
  from zoo.models.common import KerasZooModel
  from zoo.models.recommendation import Recommender
  from zoo.pipeline.api.keras.layers import *
  from zoo.pipeline.api.keras.models import *
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 1 to create a new NeuralCF object from inherited base classes: Recommender with example: obj = NeuralCF(user_count, item_count, class_num, user_embed, item_embed, hidden_layers, include_mf, mf_embed, bigdl_type)